Like any other organ of the body, the function of the brain needs to be assessed to evaluate its status in health and disease. However, unlike any other organ of the body, no good tests of brain function are available. Typical behavioral examinations include standard neurological examination, psychiatric interview, or neuropsychological testing. The electroencephalogram (EEG) provides little information unless there is major epilepsy or severely disordered brain function, as in comatose states. Methods for assessing brain structure (such as magnetic resonance imaging, MRI), chemistry (magnetic resonance spectroscopy, MRS), fluoro-deoxy-glucose based positron emission tomography (PET), or pharmacology (ligand-based PET) do not and cannot substitute for assessing brain function. Finally, “functional” MRI (fMRI) and O15—based-PET are concerned with brain areas activated in specific tasks and not about brain function per se.
Neurological disease, including for example, cognitive impairment, is a huge and growing problem. For example, in the case of the cognitive impairment known as Alzheimers disease (AD), effective intervention depends on early recognition. The amnestic form of mild cognitive impairment (MCI) is a predementia syndrome in older adults that often evolves into AD. While the clinical characterization of AD and mild cognitive impairment is usually accurate, misdiagnoses do occur, complicating research and treatment efforts.
An objective test for AD, cognitive impairment, or other neurological conditions would be desirable, but the various approaches proposed to-date have significant drawbacks, limiting their potential for application as a sensitive, reliable, diagnostic or evaluative tool.
For instance, one type of approach, as exemplified by U.S. Pat. No. 6,463,321, utilizes electroencephalogram (EEG) measurements during evoked response potential (ERP) trials. Data collected from the EEG sensors are aggregated, and a single vector representing the overall subject response to the ERP trials is produced. This vector is then compared against those of known healthy subjects and subjects with diagnosed neurological disorders, such as AD, depression, or schizophrenia. One drawback of ERP-based measurements is the evoked response to the stimulus causes certain brain regions to become very active while other brain regions remain relatively inactive. Consequently, the aggregated EEG measurements represent primarily the activated brain regions. Using this approach, a measurement representing overall brain activity, taking into account the activity of less active regions, is not possible. This problem is exacerbated by the use of conventional EEG instrumentation, which tends to detect primarily electrical activity near the outer surface of the brain, with substantially reduced sensitivity at deeper brain regions.
U.S. Pat. No. 7,177,675 discloses an approach for selecting therapies for patients diagnosed according to comparison to a database of symptomatic individuals who have had positive responses to various therapies. Quantitative neurophysiologic information such as that obtained by EEG/QEEG/MEG is compared against database records of the reference individuals to predict which course of treatment works best for someone with similar EEG/QEEG/MEG activity. However, the measurement and data analysis approaches disclosed involve mainly spectral analysis and are not capable of recognizing subtle characteristic indicia of certain diseases or conditions from among all of the measurements collected. Instead, the EEG/QEEG/MEG data, as a whole, is clustered according to treatment outcome.
In Leuthold et al., Time Series Analysis of Magnetoencephalographic Data, Exp. Brain Res., 2005, the authors describe experiments in which MEG data was acquired while subjects performed various motor tasks and experienced a variety of visual stimulation, including seeing changing images during an eye fixation task. Time domain ARIMA Box-Jenkins modeling was used to analyze the MEG data over short-term interactions of −25 to +25 ms. The data was pre-whitened, and pair-wise interactions between series of data obtained from the MEG were analyzed using a cross correlation function (CCF), an autocorrelation function (ACF) and a partial autocorrelation function (PACF). Hand movements and eye movements were monitored closely and used to relate the MEG output to the subject activity taking place. The sampling period was slightly above 1 kHz.
This work assessed the interactions between time series in pairs of sensors. Positive and negative cross-correlation patterns were observed for selected individual pairs of sensor outputs during the performing of the tasks by the subject. While this work produced some interesting insights into measurement techniques for taking MEG readings (such as the advantageous use of 1 kHz sampling, and preprocessing of the data to pre-whiten it), this work explored only individual sensor signal interactions, and did not take into account whole brain modeling in which large numbers of sensor groupings must be studied. Indeed, for reasons that will become apparent from the following disclosure, the disclosure of Leuthold et al. does not enable analyzing brain activity for purposes of characterizing a brain condition of a subject or making a diagnosis of a brain condition.
In view of these, and other drawbacks of known techniques, a practical solution is needed for automatically analyzing brain activity with the capability of reliably detecting and identifying significant neural patterns characteristic of certain conditions of interest for a variety of different subjects.